Core Clustering Research Articles

Characterization of Acinetobacter baumannii at a tertiary hospital in Guangzhou: a genomic and clinical study

Acinetobacter baumannii (AB) infections have become a global public health concern due to the continued increase in the incidence of infection and the rate of resistance to carbapenems. This study aimed to investigate the genomic features of AB strains recovered from a tertiary hospital and assess the clinical implications of the findings. A total of 217 AB strains were collected between 2016 and 2018 at a tertiary hospital in Guangzhou, with 183 (84.33%) being carbapenem-resistant AB (CRAB), with the main mechanism being the carriage of the blaOXA-23 gene. The overall mortality rate of patients caused by such strains was 15.21% (n = 33). Artificial lung ventilation and the use of meropenem were mortality risk factors in AB-infected patients, while KL2 AB infection was negatively associated. Core genome multilocus sequence typing and clustering analysis were performed on the integrated AB genome collection from the NCBI database and this study to illustrate the population structure among China. The results revealed diverse core genome profiles (n = 17) among AB strains from China, and strains from this single hospital exhibited most of the core genome profiles (n = 13), suggesting genetic variability within the hospital and transmission across the country. These findings show that the high transmission potential of the CRAB strains and meropenem usage that confers a selective advantage of CRAB clinically are two major factors that pose significant challenges to the effective clinical management of AB infections. Understanding the genetic features and transmission patterns of clinical AB strains is crucial for the effective control of infections caused by this pathogen.

Non-parameter clustering algorithm based on chain propagation and natural neighbor

Clustering analysis is a powerful tool for discovering potential knowledge in datasets. However, numerous existing clustering algorithms suffer from heavy reliance on parameter settings and cannot cluster complex manifold data very well. Moreover, although non-parameter clustering algorithms aim to lower the usage threshold, their actual performance in clustering is often unsatisfactory. Addressing how to achieve similar clustering effects for numerous data points with only a few core data points during clustering is a valuable consideration. To alleviate these challenges, a non-parameter clustering algorithm is proposed and named Non-parameter Clustering Algorithm based on Chain Propagation and Natural Neighbor (NPCCPN) in this paper, by jointly using chain propagation and natural neighbor. Specifically, NPCCPN identifies core points through chain propagation and clusters them using the saturated neighborhood graph. This makes the core data extraction and clustering process efficient and non-parameter. Finally, the performance of the method is validated on 15 complex synthetic datasets and 10 real datasets from public UCI database. The experimental results show that the effectiveness and superiority of the proposed algorithm. Purity scores first. ACC scores second without parameters, but the score first algorithm needs to set the parameters.

A review of applied research on low-carbon urban design: based on scientific knowledge mapping.

The construction of low-carbon cities is an essential component of sustainable urban development. However, there is a lack of a comprehensive low-carbon city design and evaluation system that incorporates "carbon sink accounting-remote sensing monitoring-numerical modelling-design and application" in an all-around linkage, multi-scale coupling, and localized effects. This paper utilizes the Citespace tool to evaluate low-carbon city design applications by analyzing literature in the Web of Science (WOS) core collection database. The results reveal that low-carbon cities undergo four stages: "measurement-implementation-regulation - management." The research themes are divided into three core clustering evolutionary pathways: "extension of carbon sink functions," "spatialisation of carbon sink systems," and "full-cycle, full-dimensional decarbonisation." Applications include "Utility studies of multi-scale carbon sink assessments," "Correlation analysis of carbon sink influencing factors," "Predictive characterisation of multiple planning scenarios," and "Spatial planning applications of urban sink enhancement." Future low-carbon city construction should incorporate intelligent algorithm technology in real-time to provide a strong design basis for multi-scale urban spatial design with the features of "high-precision accounting, full-cycle assessment and low-energy concept."

Carbonate Facies and Depositional Environment of Pre-Parigi Carbonate in Ardjuna Sub-basin: Application of Core-facies Coding and Electro-facies

The Pre-Parigi Formation is one of the formations in the West Java Basin North as a potential reservoir. However, the carbonate rocks of the Pre-Parigi Formation are considered to have high heterogeneity due to rock and depositional processes its diagenesis, so an appropriate approach is needed to understand its reservoir distribution. Therefore, in this study, interpretation of the facies distribution of the Pre-Parigi Formation was carried out using the integration of core data and well logs. On the core data, facies coding and clustering is performed to interpret the facies association. Then, the identified facies associations are integrated with the characters so that facies interpretation can be carried out for all intervals and all wells. The end result is an interpretation of the distribution of the facies of Pre-Parigi Carbonate. Based on the analysis results, the Pre-Parigi Carbonate in the study area is composed of five facies associations, such as deep shelf (FA 1), fore-slope (FA 2), near-reef lagoon (FA 3), lagoon (FA 4), and restricted circulation shelf – tidal flat (FA 5). FA 1 is characterized by a very high value, slightly serrated gamma-ray character. FA 2 is characterized by a high gamma-ray, serrated, intercalated with a fining-upward low gamma-ray layer, FA 3 is characterized by a low value and blocky gamma-ray, FA 4 is also characterized by a gamma-ray with a fairly low value, but has a pattern that tends to be serrated, and then FA 5 is characterized by a serrated gamma-ray character with a slightly higher value than FA 4. Based on the integration of all interpretations, the Pre-Parigi Carbonate in the study area can be divided into three depositional sequences (DS), where DS-1 develops in all area as a reef-initiation, DS-2 developed in the MVA-1 and MVA-3 wells, and did not develop in the MVA-6 well (had a give-up phase), and then DS-3 only developed in the MVA-3 well. In general, facies interpretation using the core-facies coding and electro-facies characterizing methods tends to be effective and provides more confidence interpretation. Keywords: Pre-Parigi Carbonate, Carbonate facies, Core-facies coding, Arjuna Sub-basin

Contactless medical equipment AI big data risk control and quasi thinking iterative planning

Research Background, the intelligent polymorphic system of heavy core clustering fitting iterative programming is constructed by using the edge lens of dual core heavy core. The tracking system of heavy core TANH equilibrium array is used to obtain the abnormal data range. The energy regular fluctuation of the edge lens with dual core and heavy core is used to obtain high-definition images. And build the complexity dependent parameter group from low-end equipment to high-end equipment. Heavy core clustering of hierarchical fuzzy clustering system based on differential incremental balance theory is applied to Contactless medical equipment AI big data risk control and quasi thinking iterative planning. At the same time, the mathematical model risk control is performed by fitting the TANH balance of the local nonlinear random regular micro-vibration diffusion curve. The CT/MR original data is subjected to hierarchical cross domain overlapping grid screening with the structure of fitting weakly nonlinear curve, which can capture the heavy core cluster analysis of the core layer of big data anomalies [1:10]. Successfully control the parameter group of CT/MR machine internal data, big data AI Mathematical model risk. The polar graph of high-dimensional heavy core clustering processing data is regular and scientific. The same time, it can prevent the dimension disaster caused by the construction of high-dimensional big data due to the partial loss of original data, and form a stable and predictable maintenance of CT/MR. Compared with the discrete characteristics of the polar graph of the original data. So as to correctly detect and control the dynamic change process of CT/MR in the entire life cycle. It provides help for predictive maintenance of early pre-inspection and orderly maintenance of the medical system, and developed standardized model software of automated unsupervised learning for medical big equipment big data AI Mathematical model risk control. Scientifically evaluated the exposure time and heat capacity MHU% of CT tubes, as well as the internal law of MR (nuclear magnetic resonance), and processed big data twice and three times in heavy nuclear clustering. After optimizing the algorithm, hundreds of thousands of nonlinear random vibrations are performed in the operation and maintenance database every second, and at least 30 concurrent operations are formed, which greatly improves and shortens the operation time (Yanwei et al. in J Complex 2017:1–9, 2017. https://doi.org/10.1155/2017/3437854). Finally, after adding micro-vibration quasi thinking iterative planning for the uncertain structure of AI operation, we can successfully obtain the scientific and correct results required by high-dimensional information and analyze images. This kind of AI big data risk control improves the intelligent management ability of medical institutions. Cross platform embedded web system for predictable maintenance of AI big data is established (Qi et al. in J IEEE Trans Ind Inf 99:1, 2020. https://doi.org/10.1109/tii.2020.3012157).

HEAT

Most battery-powered real-time systems today rely on a combination of specialised multicore to satisfy the needs of modern programs, which has changed the design of processing platforms dramatically in recent years. For such platforms, developing energy and thermally aware schedulers has become a top priority. As a consequence, this study presents HEAT, energy and thermal aware heuristic scheme for scheduling a group of periodic tasks on a DVFS enabled heterogeneous platform. Deadline Partitioning, Core Clustering, Temperature-Aware Scheduling, and Energy-Aware Scheduling are the four steps of the proposed scheme. In comparison to state-of-the-art, our experimental review reveals that HEAT not only enhances task set acceptance rate (as high as 3.44%) but also boosts energy savings (as high as 10.71%) and decreases temperature (as high as 7.14%) in the system.

White-Tailed Deer Spatial Distribution in Relation to '4-Poster' Tick Control Devices in Suburbia.

Deer are keystone hosts for adult ticks and have enabled the spread of tick distributions. The ‘4-Poster’ deer bait station was developed by the United States Department of Agriculture to control ticks feeding on free-ranging deer. Although effective in certain scenarios, ‘4-Poster’ deer treatment stations require the use of bait to attract deer to one location, which may cause increased deer disease transmission rates and habitat damage. To better understand and manage the impact of baited ‘4-Poster’ stations on deer movements, we captured and GPS-monitored 35 deer as part of an integrated pest management project. Fifteen ‘4-Poster’ stations were deployed among three suburban county parks to control ticks. To quantify the effects of ‘4-Poster’ stations, we calculated deer movement metrics before and after feeders were filled with whole kernel corn, and we gathered information on visitation rates to feeders. Overall, 83.3% of collared deer visited a feeder and revisited approximately every 5 days. After feeders were refilled, collared deer were ~5% closer to feeders and conspecifics than before filling. Males used a higher percentage of available feeders and visited them more throughout the deployment periods. Although these nuanced alterations in behavior may not be strong enough to increase local deer abundance, in light of infectious diseases affecting deer populations and effective ‘4-Poster’ densities, the core range shifts and clustering after refilling bait may be a cause for concern. As such, trade-offs between conflicting management goals should be carefully considered when deploying ‘4-Poster’ stations.

The Robustness of Synthetic Observations in Producing Observed Core Properties: Predictions for the TolTEC Clouds to Cores Legacy Survey

Abstract We use hydrodynamical simulations of star-forming gas with stellar feedback and sink particles—proxies for young stellar objects (YSOs)—to produce and analyze synthetic 1.1 mm continuum observations at different distances (150–1000 pc) and ages (0.49–1.27 Myr). We characterize how the inferred core properties, including mass, size, and clustering with respect to diffuse natal gas structure, change with distance, cloud evolution, and the presence of YSOs. We find that atmospheric filtering and core segmentation treatments have distance-dependent impacts on the resulting core properties for d < 300 pc and 500 pc, respectively, which dominate over evolutionary differences. Concentrating on synthetic observations at further distances (650–1000 pc), we find a growing separation between the inferred sizes and masses of cores with and without YSOs in the simulations, which is not seen in recent observations of the Monoceros R2 (Mon R2) cloud at 860 pc. We find that the synthetic cores cluster in smaller groups, and that their mass densities are correlated with gas column density over a much narrower range, than those in the Mon R2 observations. Such differences limit the applicability of the evolutionary predictions we report here, but will motivate our future efforts to adapt our synthetic observation and analysis framework to next generation simulations, such as Star Formation in Gaseous Environments (STARFORGE). These predictions and systematic characterizations will help to guide the analysis of cores on the upcoming TolTEC Clouds to Cores Legacy Survey on the Large Millimeter Telescope Alfonso Serrano.

Distance Functions Study in Fuzzy C-Means Core and Reduct Clustering

Fuzzy C-Means is a distance-based clustering process which applied by fuzzy logic concept. Clustering process worked in linear to the iteration process to minimizing the objective function. The objective function is an addition of the multiplication between the coordinates distance towards their closest cluster centroid and their membership degree. The more the iteration process, the objective function should get lower and lower. The objective of this research is to observe whether the distances which usually applied are able to fulfill the aforementioned hypothesis for determining the most suitable distance for Fuzzy C-Means clustering application. Few distance function was applied in the same dataset. 5 standard datasets and 2 random datasets were used to test the fuzzy c-means clustering performance with the 7 different distance function. Accuracy, purity, and Rand Index also applied to measure the quality of the resulted cluster. The observation result depicted that the distance function which resulted in the best quality of clusters are Euclidean, Average, Manhattan, Minkowski, Minkowski-Chebisev, and Canberra distance. These 6 distances were able to fulfill the basic hypothesis of the objective function behavior on Fuzzy C-Means Clustering method. The only distance who were not able to fulfill the basic hypothesis is Chebisev distance.

SUSTAINABLE CONSUMPTION: THEMATIC EVOLUTION FROM 1999 TO 20191

ABSTRACT Purpose: Analyze the thematic evolution of the research field on sustainable consumption from 1999 to 2019. Originality/value: The study advances the understanding of the development of the research field on sustainable consumption, analyzing the main publications and topics covered. Design/methodology/approach: Systematic literature review through a bibliometric study with analysis based on citations supported by the CitNetExplorer software, using core publication and clustering techniques. Two hundred sixty-four articles were analyzed. Findings: The most recent publications converge on the need to change the current patterns and levels of consumption and to engage dif- ferent stakeholders in participatory and co-creation processes. Also, the existence of five research clusters was identified, namely: 1. eco-efficiency with a focus on greening the market; 2. consumer behavior with an emphasis on encouraging the purchase of green products; 3. social nature of consumption that inserts the sociological perspective; 4. dynamic approach for considering the interdisciplinarity of the field; and 5. the role of education for sustainable consumption. The various themes found reveal contributions from different areas of knowledge and the importance of developing research that integrates the dynamics of the challenges to achieve sustainable consumption. We emphasize the need for an integrated approach due to the inherent complexity of the theme that involves economic, technological, political, social, psychological, and environmental aspects for governance in favor of sustainable consumption.

SEAMERS: A Semi-partitioned Energy-Aware scheduler for heterogeneous MulticorE Real-time Systems

Abstract Over the years, the nature of processing platforms is witnessing a significant shift in most of the battery supported real-time systems, which now support a combination of specialized multicores to meet the demands of modern applications. Devising energy-efficient schedulers has become a critical issue for such kinds of devices. Hence, this research presents a low-overhead heuristic strategy named SEAMERS, for DVFS based energy-aware scheduling for a set of real-time periodic tasks on a heterogeneous multicore platform. The presented strategy operates in four phases, namely Deadline Partitioning, Core Clustering, Task Allocation and Energy-Aware Scheduling. Our experimental analysis shows that the presented strategy improves upon the state-of-the-art in terms of energy savings (16% to 47% on average) and enables significant improvement in resource utilization.

Hyper-Heuristic Framework for Sequential Semi-Supervised Classification Based on Core Clustering

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization is responsible for selecting the best parameters of the core model to minimize the error. This integration aims to handle the concept drift. We designated this model as hyper-heuristic framework for semi-supervised classification or HH-F. Experimental results of the application of HH-F on real datasets prove the superiority of the proposed framework over the existing state-of-the art approaches used in the literature for sequential classification data with evolving nature.

Core Clustering as a Tool for Tackling Noise in Cluster Labels

Real-world data sets often contain mislabelled entities. This can be particularly problematic if the data set is being used by a supervised classification algorithm at its learning phase. In this case, the accuracy of this classification algorithm, when applied to unlabelled data, is likely to suffer considerably. In this paper, we introduce a clustering-based method capable of reducing the number of mislabelled entities in data sets. Our method can be summarised as follows: (i) cluster the data set; (ii) select the entities that have the most potential to be assigned to correct clusters; (iii) use the entities of the previous step to define the core clusters and map them to the labels using a confusion matrix; (iv) use the core clusters and our cluster membership criterion to correct the labels of the remaining entities. We perform numerous experiments to validate our method empirically using k-nearest neighbour classifiers as a benchmark. We experiment with both synthetic and real-world data sets with different proportions of mislabelled entities. Our experiments demonstrate that the proposed method produces promising results. Thus, it could be used as a preprocessing data correction step of a supervised machine learning algorithm.

Analysis of the Characteristics of Acupoint Selection in the Treatment of Gout by Acupuncture

To summarize and analyze the compatibility laws and the characteristics of acupoint selection in the treatment of gout by acupuncture over the past 16 years. "Gout" and "acupuncture" or "electroacupuncture" were used as search terms in the CNKI, VIP, Wanfang and PubMed databases to retrieve relevant literature. The frequency of the main acupoints and auxiliary acupoints were summarized, respectively. Association rule and hierarchical clustering analysis were used to analyze the possible rules for selecting the main acupoints. One hundred and eighteen studies met the inclusiven criteria, and involved 67 main acupoints with a frequency of all points of 630. The most commonly used acupoints were Sanyinjiao (SP 6), Ashi point, Zusanli (ST 36), Yinlingquan (SP 9), and Taichong (LR 3). The main acupoints mainly belonged to the Spleen Meridian of Foot-taiyin, Stomach Meridian of Foot-yangming and Liver Meridian of Foot-jueyin. Association rule analysis showed that Ashi point, Zusanli (ST 36) and Sanyinjiao (SP 6) had the highest correlation of all the selected acupoints. The cluster analysis showed five core clustering groups. Acupuncture treatment of gout focuses on acupoints along meridians and the acupoints around the affected joint. The main principles in the treatment of gout are invigorating Spleen, tonifying Kidney, dissipating dampness, resolving blood stasis, clearing away heat, removing toxic substances, soothing Liver, regulating qi, dredging collaterals and relieving pain.

Divide and conquer approach for semi-supervised multi-category classification through localized kernel spectral clustering

In this paper, we propose ‘divide-and-conquer approach for multi-category semi-supervised’ (DAC-MSS) classification and a novel semi-supervised binary classifier termed as ‘twin support vector machine with localized kernel spectral clustering’ (TW-LKSC). DAC-MSS builds a multi-category classifier model organized in the form of a tree of binary classifiers. The tree consists of several TW-LKSC classifiers which use a training set consisting of few labeled samples and rest unlabeled samples to generate a pair of hyperplanes, by solving a system of linear equations. The propagation of labels to unlabeled patterns is achieved through localized kernel spectral clustering (LKSC) which is the core clustering model embedded in TW-LKSC. TW-LKSC also employs cluster prototype to localize the generation of hyperplanes and prevents them from extending infinitely. The strength of DAC-MSS is its better classification accuracy and improved learning time, due to divide and conquer approach, as compared to one-against-all based semi-supervised classification algorithms. This is proved experimentally for benchmark UCI datasets. We have applied DAC-MSS for color image segmentation of images from Berkley Segmentation Dataset.

Clustering with side information: Further efforts to improve efficiency

This paper examines the issues of constrained clustering and active selection of clustering constraints in a unified approach. A fuzzy clustering method specially crafted to deal with non-spherical clusters and explicit pairwise constraints is proposed in this paper as a core clustering method. An active method for constraints selection is embedded into the core clustering method for querying beneficial constraints during clustering. The proposed approach has two major advantages relative to traditional methods. First, it considers the dependency of constraints effectiveness on the clustering algorithm by unifying both clustering and constraints selection in a uniform, principled framework. Second, a constraints selection method is embedded into the core clustering method based on the fact that constraints will be more useful if they are selected according to the current state of clustering. Experiments conducted on synthetic and real-world datasets show the effectiveness of the proposed method.

User Clustering Topic Recommendation Algorithm Based on Two Phase in the Social Network

To deal with the issues like existing common data sparseness in weibo social network and the phenomena of cold start, this paper puts forward a two-stage clustering based on the recommendation algorithm GCCR. The algorithm firstly selects users’ focused nodes which have higher number, so as to extract a dense subset of sparse data, and by using the method of graph paper, similar concerned interested core clustering is formed to this dense subset. Then, it is extracted that weibo content features of seed clustering and the whole data set other users. Then the entire user group is clustered based on content similarity. Finally the clustering results are used in subject recommendation. Through clustering the two phases of dense data subset and the whole data set, the clustering effect of extreme sparse data sets are improved. At the same time, because of fuzziness of graph clustering, this thesis retains a certain diversity in the process of user interest clustering, so as to avoid convergence too fast when cold start. This method is verified through the real social network data, and the experimental results show that this algorithm can effectively solve the problems such as data sparseness and cold start phenomenon.

A 2.5-D Memory-Logic Integration With Data-Pattern-Aware Memory Controller

This paper presents silicon interposer-based 2.5-D integration of core and memory chips. Utilization of the channels through TSVs and interposer routing between the core and memory chips is maximized by bandwidth balancing enabled by space-time multiplexing of the channels with core clustering.

A framework to study requirements-driven collaboration among agile teams: Findings from two case studies

Stakeholders’ collaboration is required to develop requirements in agile software development. Requirements engineering and agile methods share common grounds as they both focus on stakeholder collaboration. The key issue is finding a way to study collaboration driven by requirements in geographically distributed agile teams. In this paper, we aim to propose a framework to study collaboration driven by requirements among agile teams and determine the impact of their collaboration patterns on the iteration performance. We define collaboration in terms of communication as information exchange among members, and awareness knowledge of others. Two case studies were conducted to examine communication and awareness network patterns among distributed agile teams. Data were collected through questionnaires, semi-structured interviews and onsite observation. The findings revealed that the framework aids in determining the core members, collaboration trends, clustering tendency, communication and awareness reciprocity of the teams, small worldliness and centralisation behaviour of the networks and iteration performance of the agile teams. The framework has implications for the industrial practitioners, i.e. managers to learn about their team’s collaboration in order to take measures for performance improvement. At the same time, the researchers can use this framework to study other social aspects in variable settings to produce more empirical results.

A Graph Kernel Approach for Detecting Core Patents and Patent Groups

In today's business environment, competition within industries is becoming more and more intense. To survive in this fast-paced competitive environment, it's important to know what the core patents are and how the patents can be grouped. This study focuses on discovering core patents and clustering patents using a patent citation network in which core patents are represented as an influential node and patent groups as a cluster of nodes. Existing methods have discovered influential nodes and cluster nodes separately, especially in a citation network. This study develops a method used to detect influential nodes (that is, core patents) and clusters (that is, patent groups) in a patent citation network simultaneously rather than separately. The method allows a core patent in each patent group to be discovered easily and the distribution of similar patents around a core patent to be recognized. For this study, kernel k-means clustering with a graph kernel is introduced. A graph kernel helps to compute implicit similarities between patents in a high-dimensional feature space.